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How much his or her job loss influences fertility: A couple approach

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Alessandro Di Nallo discusses how job loss impacts couples’ fertility within 5 years in the UK and Germany. Women’s job loss has a significant negative effect, especially for income-egalitarian and childless couples.

Image courtesy of interviewee. December 14, 2023

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The speaker's paper investigates how job loss affects couples' decisions to have children in both the short and medium term. The research emphasizes the growing involvement of women in the job market and the impact of economic pressures on family planning. The study addresses two specific scenarios: job loss for either the male or female partner and compares these with couples who maintain stable employment.

Key findings indicate that job loss generally decreases the likelihood of couples having children in the subsequent year across both the UK and Germany. Notably, women's job loss has a more pronounced long-term negative impact on fertility compared to men's job loss. The study also highlights that factors such as income levels, earnings distribution, and parental status significantly influence how couples respond to job loss.

The research draws on extensive datasets from Germany and the UK to analyze couples over time, noting that the dynamics of family planning decisions are complex and influenced by various social and economic factors. The study concludes that job loss does not create an opportunity for couples to conceive, particularly for women, and suggests that policies should focus on protecting women's employment and enhancing family support systems to alleviate the impact of job loss on fertility decisions.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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So, the goal of I, of my, of my paper was

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to explore, a very reliable question, which is,

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how does losing a job impact on a couple's decision

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to have children in the short and the medium term?

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Uh, this question is crucial to understand, how

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family dynamics interplay

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with economic pressure in modern society,

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because, the job market involvement has increased

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for women, in the last decades.

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And many couples confront themselves

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with the uncertainty related to the liberal market.

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Uh, so, very well known, finding the literature is

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that financial stability plays a huge

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role in family planning.

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So couples decide to settle down

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and have children when they have financial stability,

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when they have economic security.

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And job loss is a disruptor of, of this economic security.

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So, more in detail, we conducted a comparison

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to understand how a job loss affects a couple's likelihood

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of having a child.

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And to do this, we looked at two scenarios separately.

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The first scenario is where the man in the couple,

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loses his job.

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And the second scenario is where the me,

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the woman in the couple experienced a job loss.

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And in each scenario, we compared,

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these couples to, similar ones where the woman, the men

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or the woman respectively, had stable

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and continuous employment.

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And, the second research goal was to,

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study which characteristics of,

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the couples are more conducive, of the consequences

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of a job loss as partners characteristics may moderate the

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effect of unemployment, instability, and fertility.

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So we decided to assess the income levels,

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how earnings are divided between the families, the, the,

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the parental status of the couples

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before the experience of job loss

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and the age of the woman in the relationship.

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So I think that one of the most important sending points

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of our paper is to focus on couples instead of individuals,

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because as I said

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before, for family planning, is a very complex decision

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and is usually performed jointly between the partners.

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So when a couple faces a job loss,

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it's not just an individual challenge is something

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that must be tackled together.

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Um, and the way, men and women view work

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and family life has changed dramatically over the years.

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We know that in the last 40, 50 years, job involvement

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of women has increased a lot,

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and women see their work as,

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very fundamental side of their life.

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Uh, so besides the economic consequences,

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there are also psychological ones

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that must be taken into account

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and possibly shape their, fertility behavior.

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Uh, the other thing that I, I, I think

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Is really relevant of our paper is

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that we don't just explore, analyze one context,

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we explore two countries.

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Um, they're quite different.

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Uh, two big economies of Western Europe,

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the UK and, Germany.

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The different in terms of labor marketing institutions,

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social safety, net, even cultural attitudes towards,

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work and family.

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Germany usually typically has more stable early career jobs

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and generous, family support system.

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The UK has, less job stability,

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early careers, less generous, generous family support.

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So overall, the UK institution look less family friendly.

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Uh, but despite of this, there's a puzzle

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because the UK has historically had higher

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birth rates than Germany.

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So our study also tries to,

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disentangle the micro mechanism that link job marketing,

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stability and fertility,

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to help us understand if this gap in fertility

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between the two, these two countries can be also partly

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explained by the,

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structural differences in job marketing stability.

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So very briefly, what do we find?

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Um, we find that job loss be mens

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or womens, generally leads to a lower likelihood

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of having children the next year,

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both in the UK and Germany.

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And we also found that statistical differences

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of fertility emerge in the medium term

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between couples whose women lost their job,

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and, with respect

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to those in which women did not have any job loss.

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But when it comes to men, there's no such a statistically,

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statistical difference.

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So in the midterm up to five years,

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there's no systematic difference

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between the couple whose men loses her, his job,

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and couples, in which men went on to work.

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So these dynamics are different between men and women.

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This means that for women is more detrimental.

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It is more detrimental to the job, which is relatively,

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novel, finding the literature

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and the explanation for this can be many,

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maybe we'll discuss them later.

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And the interesting, the other interesting results is

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that these, the dynamics affecting fertility

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after job loss are quite similar between,

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the two Western European countries, which also should

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let us, let us think to, let us, suggest that,

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probably the, the gap that we're observing fertility,

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historical fertility levels

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between these two countries is explained by other factors,

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and not just by, Java uncertainty.

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I've been quite long, but I tried to give you an overview

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of the, of the study.

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There's a sprawling literature on what are the,

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the determinants of fertility,

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and also what are the economic factors

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affecting the decision to have children.

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So I'll try to simplify according to the historical,

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economic literature, Becker.

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Um, and these fellows, parents tend

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to weigh the benefits of having children against the cost,

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both financially and emotionally.

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However, these preferences are not really in stone.

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They depend on the economic context, culture,

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gender egalitarian norms, many other factors,

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since the seventies.

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So in a era after,

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ger Becker wrote his main contribution,

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parental preferences shifted towards investing more inferior

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children, the thus prioritizing quality over quantity.

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Um, for there, there are many reasons for behind this,

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but I won't belong on this.

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Um, as a result, parents faced greater pressure

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to accumulate resources for their children's upbringing.

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And women were in incentivized to join the labor market,

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just to subsidize, men's, earnings

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in order to collect as many resources as possible

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and devote them to, their children.

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There's an interesting association

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that has emerged since 1990s.

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So the countries with the higher,

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with higher job participation, women are also those with,

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higher fertility rates.

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Um, so that means very, just simplifying a bit,

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the, the message, the more resources, the higher the income,

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the higher the propensity to have children.

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So a job loss is an unexpected loss of money,

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and that, that could, tell us

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that probably if one partner loses a job and the,

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and the income drops, the couple might decide

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to delay or not to have children.

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So the theory would suggest

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that a job loss decreases the probability of having a child.

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This is the, commonly called, the income effect,

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but there's another contra effect

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that is called substitution effect, that postulates

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that when someone loses their job,

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it may make having a child more feasible due

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to the reduced work commitment.

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Uh, this is especially relevant for women who often

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earn less than men

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and generally have less to lose when losing a job.

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So the theory would suggest that a job is not that negative

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for women, or may not be negative at all.

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So the combination of the income

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and substitution effects may be hard to predict

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and may vary according to the gender of the people

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of the person losing the job.

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So a man losing his job generally leads to fewer kids due

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to the income loss and other stress related factors.

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For women, the effect is less clear,

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and it may delay having kids due to income loss,

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or it may lead to having kids sooner if the idea

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of being out of the labor force makes child rearing seem

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more sustainable and easier.

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And on the top of this mechanism, there are other factors.

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Um, the consequences of job loss are very sprawling.

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They affect different domains such as health.

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Uh, the risk of breaking up, stress,

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all these factors are called the scaring effects,

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and they tend to make couples, so men

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and women with no distinction, less likely to have children.

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Uh, we have analyzed the, the past, empirical studies,

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and we have found mixed results.

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For example, women losing their jobs in some countries led

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to more children, while in others it didn't.

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For men, a job loss in the United States led to fewer kids,

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but nuclear effect was seen in other countries where the,

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west Fair buffers are more generous like Denmark.

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So we noticed that most

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of these studies have primarily examined the effect

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of job loss for individuals, either men

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or women without considering the

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employment status of their partners.

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Uh, this approach may have overlooked the combined effect

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of both partners employment situation on 30 decision.

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And this is the reason that motivated us to

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in inspect couples, instead

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of single individuals being affected by the job loss.

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And I think this is, one of the major contribution

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of our study with respect to the rest of the literature,

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what is less known about, in,

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in the current literature is whether this, the effect

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of job loss of women

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and men is generalizable across different sectors,

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different subgroups of the population.

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So, there's very little about whether,

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job loss is more impactful on high earning couples,

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on low earning couples, whether it is more impactful on,

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older couples or women or younger couples

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or women,

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or whether the distribution of income, across,

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across couple shapes the effect of impact loss,

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of job loss.

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And whenever these results are available, well,

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it is difficult to, draw some conclusion

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because the results are very sparse

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and sometimes are inconsistent.

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And the contribution of our study is also

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to shed light on this, no, to, to,

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analyze some moderators of,

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job loss across couple subgroups,

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which is what I said before.

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We used data sets that are household panel service.

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That means that they are,

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repeated interviews on a yearly basis.

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Um, in Germany, in the uk, in Germany,

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we use the socioeconomic panel.

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In the uk we use the British Household Panel Survey

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and Understanding Society.

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Um,

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We chose these data sets first

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because they are quite comparable.

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They're very long. Uh, they provide us with many households,

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many cases of job loss,

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and they also provide a broad range of partnerships

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and details on employment histories,

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household characteristics, even the characteristics

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that are relevant for the sub,

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subgroup divisions I was talking about.

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So they're excellent data sets.

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Uh, the other very good selling point of these data sets is

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that we could identify the reasons behind job losses,

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so we could distinguish between voluntary

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and involuntary job separations

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and this kind of job loss refer.

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The job loss we were talking about refers to a situation

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where the individuals are let go due

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to reasons beyond their control.

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So mainly involuntary, involuntary,

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like company downsizing, restructuring, plan closure,

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relocations, this is crucial to minimize the risk

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of, reverse causality,

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which I would probably talk about in a bit.

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Um, of course, there are limitations.

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The main one is that we we're not fully able

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to track the fertility of most couples,

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throughout the entire fertile period.

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Uh, because in, in some cases, couples dropped out

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before having a child or

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before reaching their desire, number of children or

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before the fertile age span ended.

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So consequently, we couldn't estimate the potential

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reduction in completely fertility,

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which is technically the realized fertility over an

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individual's entire lifetime.

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And the other, pitfall is probably the fact that

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all the job, all the instances of job loss

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cannot be considered entirely exogenous.

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Why? Because some of the job losses

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might have been anticipated.

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For example, workers may anticipate their dismissal due

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to warnings at the workplace from their employers

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or their, colleagues.

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Uh, why is this important?

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Because if a job loss is anticipated, workers,

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may decide to postpone having a child due

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to the expected economic constraint.

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So this is the reason why in the article, we don't claim

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that we measured a purely causal effect.

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Anyway, we ended up with a very large sample,

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dataset spanning from 1991 to 2020 in both countries with,

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approximately 15,000 couples in each country.

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Heterosexual couples,

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specifically those were at least one partner was employed.

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Um, so to be more clear, when investigating the impact

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of men's job loss, we looked at the couples

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where the men was employed, either as the main earner

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or in dual income couples.

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And likewise, for women's job loss, we analyzed couples

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where the woman was working alone or alongside her partner.

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So it turns out that we had two partially overlapping

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samples for each country,

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and we further split the sample into groups.

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So as I said before, those the couples

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who experienced job loss, the treaty group,

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and those who did not, the control group.

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So a couple was part

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of the treaty group if either partner faced a job loss

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during a specific year.

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Um, on the other hand, the control group consisted

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of couples who maintain some stable,

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employment status throughout,

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which includes scenarios like both partners

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working continuously

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or at least one partner, being fully

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and actively in the labor force.

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Um, the model is,

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very quite simple.

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Um, and we, looked at

15:58.465 --> 16:01.045
how likely the couples were to have child

16:01.045 --> 16:02.205
after experiencing job

16:02.335 --> 16:06.245
after experiencing a job loss, in, in the same year

16:06.245 --> 16:07.965
of job loss one year after, and so on

16:07.965 --> 16:09.565
and so forth until the fifth year.

16:10.505 --> 16:12.165
So we did the same for couples

16:12.165 --> 16:13.645
who did not experience any job loss.

16:13.645 --> 16:15.365
That is the contract group, which allowed us

16:15.365 --> 16:16.565
to compare the two scenarios.

16:17.665 --> 16:21.245
Um, we used two different statistical method,

16:21.665 --> 16:24.445
linear probability model, very, very straightforward,

16:24.985 --> 16:27.525
and another slightly more sophisticated method

16:27.705 --> 16:31.805
and inverse probability waiting that,

16:32.245 --> 16:34.485
captures the unequal probability

16:34.825 --> 16:38.605
of experiencing a job loss across observable characteristics

16:38.605 --> 16:42.565
such as age, education, job class,

16:42.705 --> 16:44.125
and other characteristics.

16:44.745 --> 16:46.365
The most important finding,

16:47.025 --> 16:49.685
the most striking finding is the gender aspect.

16:50.785 --> 16:52.725
Uh, because while both men

16:52.725 --> 16:55.885
and women's job loss impact decision in the short run,

16:57.085 --> 17:00.305
the effect is more pronounced when the woman

17:00.875 --> 17:03.865
loses her job in the midterm.

17:04.285 --> 17:07.585
So until the fifth year from the job loss.

17:08.685 --> 17:12.585
So the female job loss had a more pronounced impact,

17:12.915 --> 17:15.145
while men's, job loss

17:15.925 --> 17:18.185
didn't negatively affect birth rates,

17:18.765 --> 17:21.365
in the medium term.

17:21.755 --> 17:23.085
This is especially true in Germany.

17:24.865 --> 17:28.005
So the gender difference suggests that the loss of income,

17:28.615 --> 17:32.125
which is crucial for for t decision,

17:32.425 --> 17:35.285
is not the only factor, that matters.

17:35.705 --> 17:39.725
It is not the only factor, that comes into play.

17:39.895 --> 17:43.525
There may be other domains really important,

17:43.715 --> 17:46.365
that may affect unevenly men and women.

17:47.145 --> 17:52.045
Um, I'm talking about about career setbacks, which penalties

17:52.585 --> 17:53.925
stress,

17:54.185 --> 17:58.285
and life impacts at large or even on health.

17:58.785 --> 18:01.085
So all these effects are called scaring effects,

18:01.305 --> 18:04.645
and probably they hit women harder than men,

18:06.345 --> 18:08.885
which has not been,

18:09.795 --> 18:12.125
this results has not been highlighted in the past

18:12.365 --> 18:14.365
literature, in my opinion, because as I said

18:14.365 --> 18:18.765
before, most prior studies either either identified women's

18:18.785 --> 18:21.045
job loss or men's job loss separately.

18:22.025 --> 18:26.245
So, there was no, assessment

18:26.545 --> 18:28.845
of both job losses in the same paper.

18:30.325 --> 18:34.745
Um, the other takeaway message is that it is true.

18:35.205 --> 18:39.025
The, the, the impact of job loss varies across subgroups

18:39.025 --> 18:41.825
of the population, but generally not

18:42.525 --> 18:45.305
always in the way we expect it to be.

18:45.685 --> 18:48.625
So, for example, work specialization,

18:49.775 --> 18:53.105
does job loss affect differently couples depending on

18:53.345 --> 18:55.945
earnings division, male, female, male,

18:55.945 --> 18:57.465
female breadwinner couples

18:57.465 --> 18:58.705
or egalitarian couples,

18:58.875 --> 19:01.905
where we found mixed results in Germany, couples where men,

19:03.045 --> 19:04.585
where the main providers

19:04.765 --> 19:07.945
or where income was split more evenly between partners,

19:08.245 --> 19:09.945
had stronger reaction to job loss.

19:09.955 --> 19:13.705
While in the uk it was the couples with more equal earnings

19:13.705 --> 19:15.985
who saw a significant decrease in infertility.

19:17.085 --> 19:19.825
Uh, and this challenge is the idea that couples

19:19.825 --> 19:21.185
with more balanced income

19:21.205 --> 19:25.185
or better shielded from the economic impact of job loss

19:25.185 --> 19:28.025
because one income is temporarily and available

19:28.325 --> 19:30.585
and the capital can compensate for it.

19:31.485 --> 19:33.865
Uh, when it comes to income levels, we found

19:33.865 --> 19:36.585
that middle income couples in Germany,

19:36.935 --> 19:39.145
were more vulnerable to the effects of job loss.

19:40.405 --> 19:44.745
Uh, in the UK it was more the high to middle income couples

19:44.745 --> 19:46.425
who struggled the most, possibly

19:46.425 --> 19:49.385
because they experienced greater economic loss,

19:50.165 --> 19:51.425
parental status.

19:52.365 --> 19:56.225
We thought, you know, couples should be less responsive

19:56.225 --> 19:57.865
to job loss when their childless,

19:57.865 --> 20:01.905
because having a child is considered a milestone in one's

20:01.905 --> 20:03.705
life, and it should be achieved

20:04.655 --> 20:06.905
even if the available resources are scars.

20:07.935 --> 20:12.795
Um, yeah, but interestingly, our findings did not say this

20:13.305 --> 20:14.955
because in the UK for example,

20:15.225 --> 20:18.955
childless couples experienced a larger drop in fertility

20:19.365 --> 20:22.475
while those with one child were less, less affected.

20:22.935 --> 20:24.675
In contrast, in Germany,

20:24.775 --> 20:26.915
the effect was more in line with our expectations.

20:27.735 --> 20:32.395
And also the, the effect across women's age was,

20:32.505 --> 20:34.595
more or less consistent with our expectation

20:34.595 --> 20:38.595
because the effect was more negative, among women

20:38.665 --> 20:43.555
between 30 and 40, when, which are the stages of,

20:44.185 --> 20:45.715
steeper career advancement.

20:47.285 --> 20:49.385
So I think that the study revealed

20:49.385 --> 20:51.665
that job loss typically does not create a window

20:51.665 --> 20:53.505
of opportunity for couples to have children.

20:53.975 --> 20:55.745
This is the main takeaway, the main message.

20:56.335 --> 20:57.465
This is especially true

20:57.465 --> 20:59.185
for women experiencing their job loss.

20:59.765 --> 21:03.945
The other main takeaway I want to highlight is that the most

21:04.625 --> 21:06.825
affected groups are not always the groups

21:06.825 --> 21:09.305
that are more at risk of experiencing a job loss.

21:09.725 --> 21:13.065
So, this should suggest risk.

21:13.405 --> 21:14.585
The, the fact that risk

21:14.605 --> 21:18.025
of incurring a shock does not mirror the vulnerability

21:18.165 --> 21:20.545
to the consequences of that shock.

21:21.165 --> 21:24.985
Um, in, in other words, the impact

21:25.165 --> 21:28.665
of job loss on fertility isn't just limited

21:28.845 --> 21:31.345
to the traditionally disadvantaged groups,

21:32.085 --> 21:35.305
but also may affect those who may have more to lose.

21:35.685 --> 21:38.865
Anyway. This is just a, this is just suggestive evidence

21:39.325 --> 21:41.425
and should be tested in future studies.

21:42.645 --> 21:46.585
Uh, from a policy point of view, I would just like

21:46.585 --> 21:48.865
to stress out that,

21:49.585 --> 21:52.985
policies should focus on protective protecting the labor

21:52.985 --> 21:55.145
market position of women

21:55.325 --> 21:57.465
who experience temporary job setbacks,

21:57.465 --> 21:59.225
because this is the category

21:59.225 --> 22:01.745
that they're the brands of job losses.

22:03.045 --> 22:06.065
And this policy should be, should go hand in hand

22:06.065 --> 22:08.585
with improving unemployment, unemployment benefits,

22:08.585 --> 22:09.585
especially in the uk,

22:10.805 --> 22:12.785
but also other policies such

22:12.785 --> 22:14.705
as providing affordable childcare

22:14.925 --> 22:17.265
or flexible working arrangement that

22:17.785 --> 22:19.505
probably don't take all the risk

22:19.505 --> 22:21.825
of incurring job loss are also crucial

22:21.825 --> 22:26.265
because they help couples balance work and family life,

22:26.445 --> 22:30.105
and they reduce the cost of fertility.

22:30.445 --> 22:35.145
So they provide, implicit, economic buffer

22:35.325 --> 22:36.105
for the couples.

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